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Modern Applied Statistics with S-Plus (Venebles)

 
 Author(s)  W. N. Venables, B. D. Ripley
 Title  Modern Applied Statistics with S-Plus
 Edition  
 Year  1994
 Publisher  Springer
 ISBN  0-387-94350-1
 Website  www.springer.com
 book link
 http://www.stats.ox.ac.uk/~ripley/   (Brian Ripley's home page)
 




Table of Contents


Preface

Typographical Conventions

1 Introduction
1.1 A Quick Overview of S
1.2 Using S
1.3 An Introductory Session
1.4 What Next?

2 Data Manipulation
2.1 Objects
2.2 Connections
2.3 Data Manipulation
2.4 Tables and Cross-Classification

3 TheS Language
3.1 Language Layout
3.2 More on S Objects
3.3 Arithmetical Expressions
3.4 Character Vector Operations
3.5 Formatting and Printing
3.6 Calling Conventions for Functions
3.7 Model Formulae
3.8 Control Structures
3.9 Array and Matrix Operations
3.10 Introduction to Classes and Methods

4 Graphics
4.1 Graphics Devices
4.2 Basic Plotting Functions
4.3 Enhancing Plots
4.4 Fine Control of Graphics
4.5 Trellis Graphics

5 Univariate Statistics
5.1 Probability Distributions
5.2 Generating Random Data
5.3 Data Summaries
5.4 Classical Univariate Statistics
5.5 Robust Summaries
5.6 Density Estimation
5.7 Bootstrap and Permutation Methods

6 Linear Statistical Models
6.1 An Analysis of Covariance Example
6.2 Model Formulae and Model Matrices
6.3 Regression Diagnostics
6.4 Safe Prediction
6.5 Robust and Resistant Regression
6.6 Bootstrapping Linear Models
6.7 Factorial Designs and Designed Experiments
6.8 An Unbalanced Four-Way Layout
6.9 Predicting Computer Performance
6.10 Multiple Comparisons

7 Generalized LinearModels
7.1 Functions for Generalized Linear Modelling
7.2 Binomial Data
7.3 Poisson and Multinomial Models
7.4 A Negative Binomial Family
7.5 Over-Dispersion in Binomial and Poisson GLMs

8 Non-Linear and Smooth Regression
8.1 An Introductory Example
8.2 Fitting Non-Linear Regression Models
8.3 Non-Linear Fitted Model Objects and Method Functions
8.4 Confidence Intervals for Parameters
8.5 Profiles
8.6 Constrained Non-Linear Regression
8.7 One-Dimensional Curve-Fitting
8.8 Additive Models
8.9 Projection-Pursuit Regression
8.10 Neural Networks
8.11 Conclusions

9 Tree-Based Methods
9.1 Partitioning Methods
9.2 Implementation in rpart
9.3 Implementation in tree

10 Random and Mixed Effects
10.1 Linear Models
10.2 Classic Nested Designs
10.3 Non-Linear Mixed Effects Models
10.4 Generalized Linear Mixed Models
10.5 GEE Models

11 Exploratory Multivariate Analysis
11.1 Visualization Methods
11.2 Cluster Analysis
11.3 Factor Analysis
11.4 Discrete Multivariate Analysis

12 Classification
12.1 Discriminant Analysis
12.2 Classification Theory
12.3 Non-Parametric Rules
12.4 Neural Networks
12.5 Support Vector Machines
12.6 Forensic Glass Example
12.7 Calibration Plots

13 Survival Analysis
13.1 Estimators of Survivor Curves
13.2 Parametric Models
13.3 Cox Proportional Hazards Model
13.4 Further Examples

14 Time Series Analysis
14.1 Second-Order Summaries
14.2 ARIMA Models
14.3 Seasonality
14.4 Nottingham Temperature Data
14.5 Regression with Autocorrelated Errors 1
14.6 Models for Financial Series

15 Spatial Statistics
15.1 Spatial Interpolationand Smoothing
15.2 Kriging
15.3 Point Process Analysis

16 Optimization
16.1 Univariate Functions
16.2 Special-Purpose Optimization Functions
16.3 General Optimization

Appendices

A Implementation-Specific Details
A.1 Using S-PLUS under Unix / Linux
A.2 Using S-PLUS under Windows
A.3 Using R under Unix / Linux
A.4 Using R under Windows
A.5 For Emacs Users

B The S-PLUS GUI

C Datasets, Software and Libraries
C.1 Our Software
C.2 Using Libraries

References
Index




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